latest/index.bs

<pre class='metadata'>
Title: Next-generation file formats (NGFF)
Shortname: ome-ngff
Level: 1
Status: LS-COMMIT
Status: w3c/ED
Group: ome
URL: https://ngff.openmicroscopy.org/latest/
Repository: https://github.com/ome/ngff
Issue Tracking: Forums https://forum.image.sc/tag/ome-ngff
Logo: http://www.openmicroscopy.org/img/logos/ome-logomark.svg
Local Boilerplate: header yes
Local Boilerplate: copyright yes
Boilerplate: style-darkmode off
Markup Shorthands: markdown yes
Editor: Josh Moore, Open Microscopy Environment (OME) https://www.openmicroscopy.org
Editor: Sébastien Besson, Open Microscopy Environment (OME) https://www.openmicroscopy.org
Editor: Constantin Pape, European Molecular Biology Laboratory (EMBL) https://www.embl.org/sites/heidelberg/
Abstract: This document contains next-generation file format (NGFF)
Abstract: specifications for storing bioimaging data in the cloud.
Abstract: All specifications are submitted to the https://image.sc community for review.
Status Text: The current released version of this specification is
Status Text: <a href="../0.3/index.html">0.3</a>. Migration scripts
Status Text: will be provided between numbered versions. Data written with these latest changes
Status Text: (an "editor's draft") will not necessarily be supported.
</pre>

Introduction {#intro}
=====================

Bioimaging science is at a crossroads. Currently, the drive to acquire more,
larger, preciser spatial measurements is unfortunately at odds with our ability
to structure and share those measurements with others. During a global pandemic
more than ever, we believe fervently that global, collaborative discovery as
opposed to the post-publication, "data-on-request" mode of operation is the
path forward. Bioimaging data should be shareable via open and commercial cloud
resources without the need to download entire datasets.

At the moment, that is not the norm. The plethora of data formats produced by
imaging systems are ill-suited to remote sharing. Individual scientists
typically lack the infrastructure they need to host these data themselves. When
they acquire images from elsewhere, time-consuming translations and data
cleaning are needed to interpret findings. Those same costs are multiplied when
gathering data into online repositories where curator time can be the limiting
factor before publication is possible. Without a common effort, each lab or
resource is left building the tools they need and maintaining that
infrastructure often without dedicated funding.

This document defines a specification for bioimaging data to make it possible
to enable the conversion of proprietary formats into a common, cloud-ready one.
Such next-generation file formats layout data so that individual portions, or
"chunks", of large data are reference-able eliminating the need to download
entire datasets.


Why "<dfn export="true"><abbr title="Next-generation file-format">NGFF</abbr></dfn>"? {#why-ngff}
-------------------------------------------------------------------------------------------------

A short description of what is needed for an imaging format is "a hierarchy
of n-dimensional (dense) arrays with metadata". This combination of features
is certainly provided by <dfn export="true"><abbr title="Hierarchical Data Format 5">HDF5</abbr></dfn>
from the <a href="https://www.hdfgroup.org">HDF Group</a>, which a number of
bioimaging formats do use. HDF5 and other larger binary structures, however,
are ill-suited for storage in the cloud where accessing individual chunks
of data by name rather than seeking through a large file is at the heart of
parallelization.

As a result, a number of formats have been developed more recently which provide
the basic data structure of an HDF5 file, but do so in a more cloud-friendly way.
In the [PyData](https://pydata.org/) community, the Zarr [[zarr]] format was developed
for easily storing collections of [NumPy](https://numpy.org/) arrays. In the
[ImageJ](https://imagej.net/) community, N5 [[n5]] was developed to work around
the limitations of HDF5 ("N5" was originally short for "Not-HDF5").
Both of these formats permit storing individual chunks of data either locally in
separate files or in cloud-based object stores as separate keys.

A [current effort](https://zarr-specs.readthedocs.io/en/core-protocol-v3.0-dev/protocol/core/v3.0.html)
is underway to unify the two similar specifications to provide a single binary
specification. The editor's draft will soon be entering a [request for comments (RFC)](https://github.com/zarr-developers/zarr-specs/issues/101) phase with the goal of having a first version early in 2021. As that
process comes to an end, this document will be updated.

OME-NGFF {#ome-ngff}
--------------------

The conventions and specifications defined in this document are designed to
enable next-generation file formats to represent the same bioimaging data
that can be represented in \[OME-TIFF](http://www.openmicroscopy.org/ome-files/)
and beyond. However, the conventions will also be usable by HDF5 and other sufficiently advanced
binary containers. Eventually, we hope, the moniker "next-generation" will no longer be
applicable, and this will simply be the most efficient, common, and useful representation
of bioimaging data, whether during acquisition or sharing in the cloud.

Note: The following text makes use of OME-Zarr [[ome-zarr-py]], the current prototype implementation,
for all examples.

On-disk (or in-cloud) layout {#on-disk}
=======================================

An overview of the layout of an OME-Zarr fileset should make
understanding the following metadata sections easier. The hierarchy
is represented here as it would appear locally but could equally
be stored on a web server to be accessed via HTTP or in object storage
like S3 or GCS.

Images {#image-layout}
----------------------

The following layout describes the expected Zarr hierarchy for images with
multiple levels of resolutions and optionally associated labels.

```
.                             # Root folder, potentially in S3,
│                             # with a flat list of images by image ID.
│
├── 123.zarr                  # One image (id=123) converted to Zarr.
│
└── 456.zarr                  # Another image (id=456) converted to Zarr.
    │
    ├── .zgroup               # Each image is a Zarr group, or a folder, of other groups and arrays.
    ├── .zattrs               # Group level attributes are stored in the .zattrs file and include
    │                         # "multiscales" and "omero" (see below). In addition, the group level attributes
    │                         # must also contain "_ARRAY_DIMENSIONS" if this group directly contains multi-scale arrays.
    │
    ├── 0                     # Each multiscale level is stored as a separate Zarr array,
    │   ...                   # which is a folder containing chunk files which compose the array.
    ├── n                     # The name of the array is arbitrary with the ordering defined by
    │   │                     # by the "multiscales" metadata, but is often a sequence starting at 0.
    │   │
    │   ├── .zarray           # All image arrays must be up to 5-dimensional
    │   │                     # with dimension order (t, c, z, y, x).
    │   │
    │   └─ t                  # Chunks are stored with the nested directory layout.
    │      └─ c               # All but the last chunk element are stored as directories.
    │         └─ z            # The terminal chunk is a file. Together the directory and file names
    │            └─ y         # provide the "chunk coordinate" (t, c, z, y, x), where the maximum coordinate
    │               └─ x      # will be `dimension_size / chunk_size`.
    │
    └── labels
        │
        ├── .zgroup           # The labels group is a container which holds a list of labels to make the objects easily discoverable
        │
        ├── .zattrs           # All labels will be listed in `.zattrs` e.g. `{ "labels": [ "original/0" ] }`
        │                     # Each dimension of the label `(t, c, z, y, x)` should be either the same as the
        │                     # corresponding dimension of the image, or `1` if that dimension of the label
        │                     # is irrelevant.
        │
        └── original          # Intermediate folders are permitted but not necessary and currently contain no extra metadata.
            │
            └── 0             # Multiscale, labeled image. The name is unimportant but is registered in the "labels" group above.
                ├── .zgroup   # Zarr Group which is both a multiscaled image as well as a labeled image.
                ├── .zattrs   # Metadata of the related image and as well as display information under the "image-label" key.
                │
                ├── 0         # Each multiscale level is stored as a separate Zarr array, as above, but only integer values
                │   ...       # are supported.
                └── n
```

High-content screening {#hcs-layout}
------------------------------------

The following specification defines the hierarchy for a high-content screening
dataset. Three groups must be defined above the images:

-   the group above the images defines the well and MUST implement the
    [well specification](#well-md). All images contained in a well are fields
    of view of the same well
-   the group above the well defines a row of wells
-   the group above the well row defines an entire plate i.e. a two-dimensional
    collection of wells organized in rows and columns. It MUST implement the
    [plate specification](#plate-md)


```
.                             # Root folder, potentially in S3,
│
└── 5966.zarr                 # One plate (id=5966) converted to Zarr
    ├── .zgroup
    ├── .zattrs               # Implements "plate" specification
    ├── A                     # First row of the plate
    │   ├── .zgroup
    │   │
    │   ├── 1                 # First column of row A
    │   │   ├── .zgroup
    │   │   ├── .zattrs       # Implements "well" specification
    │   │   │
    │   │   ├── 0             # First field of view of well A1
    │   │   │   │
    │   │   │   ├── .zgroup
    │   │   │   ├── .zattrs   # Implements "multiscales", "omero"
    │   │   │   ├── 0
    │   │   │   │   ...       # Resolution levels
    │   │   │   ├── n
    │   │   │   └── labels    # Labels (optional)
    │   │   ├── ...           # Fields of view
    │   │   └── m
    │   ├── ...               # Columns
    │   └── 12
    ├── ...                   # Rows
    └── H
```

Metadata {#metadata}
====================

The various `.zattrs` files throughout the above array hierarchy may contain metadata
keys as specified below for discovering certain types of data, especially images.

"axes" metadata {#axes-md}
--------------------------

Describes axes of a physical coordinate space. It is a dictionary, which MUST contain the fields:
- "labels": list of strings that specify the name per dimension. The values MUST be unique.
- "types": list of strings that specify the type per dimension. The values SHOULD be one of "space", "channel", "time".
- "units": list of strings that specify the unit per dimension.

The three lists MUST have the same length.
If part of [[#multiscale-md]], the length MUST be equal to the array.


"transformation" metadata {#trafo-md}
-------------------------------------

Describes transformations applied to an array. It is a dictionary, which MUST contain the field "type".
The value of "type" MUST be one of the elements of the "type" column in the table below.
Additional fields are defined by the column "fields".

| type          | fields | description |
|-              |-       |-            |
| `identity`    |        | identity transformation, is the default transformation and is typically not explicitly defined |
| `translation` | one of: `"translation":List[float]`, `"path":str` | translation vector, stored either as a list of floats (`"translation"`) or as binary data at a location in this container (`path`). The length of vector defines number of dimensions. |
| `scale`       | one of: `"scale":List[float]`, `"path":str` | scale vector, stored either as a list of floats (`scale`) or as binary data at a location in this container (`path`). The length of vector defines number of dimensions. |
| `affine`      | one of: `"affine":List[float]`, `"path:str"` | affine transformation matrix defined as list consisting of n sets of n + 1 scalar numbers, stored either as a list of floats (`affine`) or as binary data at a location in this container (`path`). n is number of dimensions |
| `d_field`     | `"path:str"` | deformation / displacement field storing one offset for each grid coordinate, `path` points to the array in this container that stores the field |
| `p_field`     | `"path:str"` | position field storing one absolute position for each grid coordinate,  `path` points to the array in this container that stores the field |

In addition, the field "axisIndices" MAY be given to specify the subset of axes that the transformation is applied to, leaving other axes unchanged. If not given, the transformation is applied to all axes. The length of "axisIndices" MUST be equal to the dimensionality of the transformation. If "axisIndices" are not given, the dimensionality of the transformation MUST be equal to the number of dimensions of the array.


"multiscales" metadata {#multiscale-md}
---------------------------------------

Metadata about the multiple resolution representations of the image can be
found under the "multiscales" key in the group-level metadata.

"multiscales" contains a list of dictionaries where each entry describes a multiscale image.

Each dictionary contained in the list MUST contain the field "datasets", which is a list of dictionaries describing
the arrays storing the individual resolution levels.
Each dictionary in "datasets" MUST contain the field "path", whose value contains the path to the array for this resolution relative
to the current zarr group. The "path"s MUST be ordered from largest (i.e. highest resolution) to smallest.
All arrays MUST have the same number of dimensions and MUST NOT have more than 5 dimensions.
Each dictionary MAY contain the field "transformations", which contains a list of [[#trafo-md]] that specify the transformation from data coordinates to the physical coordinates (as specified by "axes"). 
If given, the transformations MUST only be of type `identity`, `translation` or `scale`. This restriction ensures a simple mapping from data space to physical space and in particular that the voxel size can be directly read if a "scale" transformation is given.
The transformations are applied sequentially and in order. If not given, the identity transformation is assumed.

It MUST contain the field "axes", see [[#axes-md]] and the length of the lists in "axes" must be equal to the number of dimensions in the array.
The "labels" list must be repeated in the field "_ARRAY_DIMENSIONS" of all scale groups (i.e. groups containing arrays with the multiscale data).
This ensures compatibility with the [xarray zarr encoding](http://xarray.pydata.org/en/stable/internals/zarr-encoding-spec.html#zarr-encoding).

It SHOULD contain the field "name".

It SHOULD contain the field "version", which indicates the version of the
multiscale metadata of this image (current version is 0.3).

It SHOULD contain the field "type", which gives the type of downscaling method used to generate the multiscale image pyramid.

It SHOULD contain the field "metadata", which contains a dictionary with additional information about the downscaling method.

```json
{
    "multiscales": [
        {
            "version": "0.3",
            "name": "example",
            "datasets": [
                {"path": "0"},
                {"path": "1"},
                {"path": "2"}
            ],
            "axes": [
                "t", "c", "z", "y", "x"
            ],
            "type": "gaussian",
            "metadata": {                                       # the fields in metadata depend on the downscaling implementation
                "method": "skimage.transform.pyramid_gaussian", # here, the paramters passed to the skimage function are given
                "version": "0.16.1",
                "args": "[true]",
                "kwargs": {"multichannel": true}
            }
        }
    ]
}
```

If only one multiscale is provided, use it. Otherwise, the user can choose by
name, using the first multiscale as a fallback:

```python
datasets = []
for named in multiscales:
    if named["name"] == "3D":
        datasets = [x["path"] for x in named["datasets"]]
        break
if not datasets:
    # Use the first by default. Or perhaps choose based on chunk size.
    datasets = [x["path"] for x in multiscales[0]["datasets"]]
```

"omero" metadata {#omero-md}
----------------------------

Information specific to the channels of an image and how to render it
can be found under the "omero" key in the group-level metadata:

```json
"id": 1,                              # ID in OMERO
"name": "example.tif",                # Name as shown in the UI
"version": "0.3",                     # Current version
"channels": [                         # Array matching the c dimension size
    {
        "active": true,
        "coefficient": 1,
        "color": "0000FF",
        "family": "linear",
        "inverted": false,
        "label": "LaminB1",
        "window": {
            "end": 1500,
            "max": 65535,
            "min": 0,
            "start": 0
        }
    }
],
"rdefs": {
    "defaultT": 0,                    # First timepoint to show the user
    "defaultZ": 118,                  # First Z section to show the user
    "model": "color"                  # "color" or "greyscale"
}
```

See https://docs.openmicroscopy.org/omero/5.6.1/developers/Web/WebGateway.html#imgdata
for more information.

"labels" metadata {#labels-md}
------------------------------

The special group "labels" found under an image Zarr contains the key `labels` containing
the paths to label objects which can be found underneath the group:

```json
{
  "labels": [
    "orphaned/0"
  ]
}
```

Unlisted groups MAY be labels.

"image-label" metadata {#label-md}
----------------------------------

Groups containing the `image-label` dictionary represent an image segmentation
in which each unique pixel value represents a separate segmented object.
`image-label` groups MUST also contain `multiscales` metadata and the two
"datasets" series MUST have the same number of entries.

The `colors` key defines a list of JSON objects describing the unique label
values. Each entry in the list MUST contain the key "label-value" with the
pixel value for that label. Additionally, the "rgba" key MAY be present, the
value for which is an RGBA unsigned-int 4-tuple: `[uint8, uint8, uint8, uint8]`
All `label-value`s must be unique. Clients who choose to not throw an error
should ignore all except the _last_ entry.

Some implementations may represent overlapping labels by using a specially assigned
value, for example the highest integer available in the pixel range.

The `properties` key defines a list of JSON objects which also describes the unique 
label values. Each entry in the list MUST contain the key "label-value" with the 
pixel value for that label. Additionally, an arbitrary number of key-value pairs 
MAY be present for each label value denoting associated metadata. Not all label 
values must share the same key-value pairs within the properties list.

The `source` key is an optional dictionary which contains information on the
image the label is associated with. If included it MAY include a key `image`
whose value is the relative path to a Zarr image group. The default value is
"../../" since most labels are stored under a subgroup named "labels/" (see
above).


```json
"image-label":
  {
    "version": "0.3",
    "colors": [
      {
        "label-value": 1,
        "rgba": [255, 255, 255, 0]
      },
      {
        "label-value": 4,
        "rgba": [0, 255, 255, 128]
      },
      ...
      ],
    "properties": [
      {
        "label-value": 1,
        "area (pixels)": 1200,
        "class": "foo"

      },
      {
        "label-value": 4,
        "area (pixels)": 1650
      },
      ...
      ]
  },
  "source": {
    "image": "../../"
  }
]
```

"plate" metadata {#plate-md}
----------------------------

For high-content screening datasets, the plate layout can be found under the
custom attributes of the plate group under the `plate` key.

<dl>
  <dt><strong>acquisitions</strong></dt>
  <dd>An optional list of JSON objects defining the acquisitions for a given
      plate. Each acquisition object MUST contain an `id` key providing an
      unique identifier within the context of the plate to which fields of
      view can refer to. It SHOULD contain  a `name` key identifying the name
      of the acquisition. It SHOULD contain a `maximumfieldcount` key
      indicating the maximum number of fields of view for the acquisition. It
      MAY contain a `description` key providing a description for the
      acquisition. It MAY contain a `startime` and/or `endtime` key specifying
      the start and/or end timestamp of the acquisition using an epoch
      string.</dd>
  <dt><strong>columns</strong></dt>
  <dd>A list of JSON objects defining the columns of the plate. Each column
      object defines the properties of the column at the index of the object
      in the list. If not empty, it MUST contain a `name` key specifying the
      column name.</dd>
  <dt><strong>field_count</strong></dt>
  <dd>An integer defining the maximum number of fields per view across all
      wells.</dd>
  <dt><strong>name</strong></dt>
  <dd>A string defining the name of the plate.</dd>
  <dt><strong>rows</strong></dt>
  <dd>A list of JSON objects defining the rows of the plate. Each row object
      defines the properties of the row at the index of the object in the
      list. If not empty, it MUST contain a `name` key specifying the row
      name.</dd>
  <dt><strong>version</strong></dt>
  <dd>A string defining the version of the specification.</dd>
  <dt><strong>wells</strong></dt>
  <dd>A list of JSON objects defining the wells of the plate. Each well object 
      MUST contain a `path` key identifying the path to the well subgroup.</dd>
</dl>

For example the following JSON object defines a plate with two acquisition and
6 wells (2 rows and 3 columns), containing up 2 fields of view per acquistion.

```json
"plate": {
    "acquisitions": [
        {
            "id": 1,
            "maximumfieldcount": 2,
            "name": "Meas_01(2012-07-31_10-41-12)",
            "starttime": 1343731272000
        },
        {
            "id": 2,
            "maximumfieldcount": 2,
            "name": "Meas_02(201207-31_11-56-41)",
            "starttime": 1343735801000
        }
    ],
    "columns": [
        {
            "name": "1"
        },
        {
            "name": "2"
        },
        {
            "name": "3"
        }
    ],
    "field_count": 4,
    "name": "test",
    "rows": [
        {
            "name": "A"
        },
        {
            "name": "B"
        }
    ],
    "version": "0.3",
    "wells": [
        {
            "path": "2020-10-10/A/1"
        },
        {
            "path": "2020-10-10/A/2"
        },
        {
            "path": "2020-10-10/A/3"
        },
        {
            "path": "2020-10-10/B/1"
        },
        {
            "path": "2020-10-10/B/2"
        },
        {
            "path": "2020-10-10/B/3"
        }
    ]
  }
```

"well" metadata {#well-md}
--------------------------

For high-content screening datasets, the metadata about all fields of views
under a given  well can be found under the "well" key in the attributes of the
well group.

<dl>
  <dt><strong>images</strong></dt>
  <dd>A list of JSON objects defining the fields of views for a given well.
      Each object MUST contain a `path` key identifying the path to the
      field of view. If multiple acquisitions were performed in the plate, it  
      SHOULD contain an `acquisition` key identifying the id of the 
      acquisition which must match one of acquisition JSON objects defined in
      the plate metadata.</dd>
  <dt><strong>version</strong></dt>
  <dd>A string defining the version of the specification.</dd>
</dl>

For example the following JSON object defines a well with four fields of
views. The first two fields of view were part of the first acquisition while
the last two fields of view were part of the second acquisition.

```json
"well": {
    "images": [
        {
            "acquisition": 1,
            "path": "0"
        },
        {
            "acquisition": 1,
            "path": "1"
        },
        {
            "acquisition": 2,
            "path": "2"
        },
        {
            "acquisition": 2,
            "path": "3"
        }
    ],
    "version": "0.3"
  }
```

Implementations {#implementations}
==================================

Projects which support reading and/or writing OME-NGFF data include:

<dl>

  <dt><strong>[bigdataviewer-ome-zarr](https://github.com/mobie/bigdataviewer-ome-zarr)</strong></dt>
  <dd>Fiji-plugin for reading OME-Zarr.</dd>

  <dt><strong>[bioformats2raw](https://github.com/glencoesoftware/bioformats2raw)</strong></dt>
  <dd>A performant, Bio-Formats image file format converter.</dd>

  <dt><strong>[omero-ms-zarr](https://github.com/ome/omero-ms-zarr)</strong></dt>
  <dd>A microservice for OMERO.server that converts images stored in OMERO to OME-Zarr files on the fly, served via a web API.</dd>

  <dt><strong>[idr-zarr-tools](https://github.com/IDR/idr-zarr-tools)</strong></dt>
  <dd>A full workflow demonstrating the conversion of IDR images to OME-Zarr images on S3.</dd>

  <dt><strong>[OMERO CLI Zarr plugin](https://github.com/ome/omero-cli-zarr)</strong></dt>
  <dd>An OMERO CLI plugin that converts images stored in OMERO.server into a local Zarr file.</dd>

  <dt><strong>[ome-zarr-py](https://github.com/ome/ome-zarr-py)</strong></dt>
  <dd>A napari plugin for reading ome-zarr files.</dd>

  <dt><strong>[vizarr](https://github.com/hms-dbmi/vizarr/)</strong></dt>
  <dd>A minimal, purely client-side program for viewing Zarr-based images with Viv & ImJoy.</dd>

</dl>

<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png"
    alt="Diagram of related projects"/>

All implementations prevent an equivalent representation of a dataset which can be downloaded or uploaded freely. An interactive
version of this diagram is available from the [OME2020 Workshop](https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/).
Mouseover the blackboxes representing the implementations above to get a quick tip on how to use them.

Note: If you would like to see your project listed, please open an issue or PR on the [ome/ngff](https://github.com/ome/ngff) repository.

Citing {#citing}
================

[Next-generation file format (NGFF) specifications for storing bioimaging data in the cloud.](https://ngff.openmicroscopy.org/0.3)
J. Moore, *et al*. Editors. Open Microscopy Environment Consortium, 24 August 2021.
This edition of the specification is [https://ngff.openmicroscopy.org/0.3/](https://ngff.openmicroscopy.org/0.3/]).
The latest edition is available at [https://ngff.openmicroscopy.org/latest/](https://ngff.openmicroscopy.org/latest/).
[(doi:10.5281/zenodo.4282107)](https://doi.org/10.5281/zenodo.4282107)

Version History {#history}
==========================

<table>
  <thead>
    <tr>
      <td>Revision</td>
      <td>Date</td>
      <td>Description</td>
    </tr>
  </thead>
  <tr>
    <td>0.3.0</td>
    <td>2021-08-24</td>
    <td>Add axes field to multiscale metadata     </td>
  </tr>
  <tr>
    <td>0.2.0</td>
    <td>2021-03-29</td>
    <td>Change chunk dimension separator to "/"   </td>
  </tr>
  <tr>
    <td>0.1.4</td>
    <td>2020-11-26</td>
    <td>Add HCS specification                      </td>
  </tr>
  <tr>
    <td>0.1.3</td>
    <td>2020-09-14</td>
    <td>Add labels specification                   </td>
  </tr>
  <tr>
    <td>0.1.2     </td>
    <td>2020-05-07</td>
    <td>Add description of "omero" metadata        </td>
  </tr>
  <tr>
    <td>0.1.1     </td>
    <td>2020-05-06</td>
    <td>Add info on the ordering of resolutions    </td>
  </tr>
  <tr>
    <td>0.1.0     </td>
    <td>2020-04-20</td>
    <td>First version for internal demo            </td>
  </tr>
</table>


<pre class="biblio">
{
  "blogNov2020": {
    "href": "https://blog.openmicroscopy.org/file-formats/community/2020/11/04/zarr-data/",
    "title": "Public OME-Zarr data (Nov. 2020)",
    "authors": [
      "OME Team"
    ],
    "status": "Informational",
    "publisher": "OME",
    "id": "blogNov2020",
    "date": "04 November 2020"
  },
  "imagesc26952": {
    "href": "https://forum.image.sc/t/ome-s-position-regarding-file-formats/26952",
    "title": "OME’s position regarding file formats",
    "authors": [
      "OME Team"
    ],
    "status": "Informational",
    "publisher": "OME",
    "id": "imagesc26952",
    "date": "19 June 2020"
  },
  "n5": {
    "id": "n5",
    "href": "https://github.com/saalfeldlab/n5/issues/62",
    "title": "N5---a scalable Java API for hierarchies of chunked n-dimensional tensors and structured meta-data",
    "status": "Informational",
    "authors": [
      "John A. Bogovic",
      "Igor Pisarev",
      "Philipp Hanslovsky",
      "Neil Thistlethwaite",
      "Stephan Saalfeld"
    ],
    "date": "2020"
  },
  "ome-zarr-py": {
    "id": "ome-zarr-py",
    "href": "https://doi.org/10.5281/zenodo.4113931",
    "title": "ome-zarr-py: Experimental implementation of next-generation file format (NGFF) specifications for storing bioimaging data in the cloud.",
    "status": "Informational",
    "publisher": "Zenodo",
    "authors": [
      "OME",
      "et al"
    ],
    "date": "06 October 2020"
  },
  "zarr": {
    "id": "zarr",
    "href": "https://doi.org/10.5281/zenodo.4069231",
    "title": "Zarr: An implementation of chunked, compressed, N-dimensional arrays for Python.",
    "status": "Informational",
    "publisher": "Zenodo",
    "authors": [
      "Alistair Miles",
      "et al"
    ],
    "date": "06 October 2020"
  }
}
</pre>